# Common Analysis Patterns

Multi-factor designs, contrasts, interaction terms, continuous covariates, and paired or
batch-aware designs.

## Common Analysis Patterns

### Two-Group Comparison

Standard case-control comparison:

```python
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition")
dds.deseq2()

ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()

results = ds.results_df
significant = results[results.padj < 0.05]
```

### Multiple Comparisons

Testing multiple treatment groups against control:

```python
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition")
dds.deseq2()

treatments = ["treatment_A", "treatment_B", "treatment_C"]
all_results = {}

for treatment in treatments:
    ds = DeseqStats(dds, contrast=["condition", treatment, "control"])
    ds.summary()
    all_results[treatment] = ds.results_df

    sig_count = len(ds.results_df[ds.results_df.padj < 0.05])
    print(f"{treatment}: {sig_count} significant genes")
```

### Accounting for Batch Effects

Control for technical variation:

```python
# Include batch in design
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~batch + condition")
dds.deseq2()

# Test condition while controlling for batch
ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()
```

### Continuous Covariates

Include continuous variables like age or dosage:

```python
# Ensure continuous variable is numeric
metadata["age"] = pd.to_numeric(metadata["age"])

dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~age + condition")
dds.deseq2()

ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()
```